A multi-step genetic algorithm is developed to eliminate the limitations of the
inverse analysis method for detecting the corrosion profile on the steel matrices
from a small number of potential data on the concrete structure. The corrosion
profile represents the the number, locations and shapes of plural corrosion parts
on the steel matrices. It was modeled into a binary string which is defined by
discretizing the steel matrices into a suitable number of segments using a certain
resolution. Each segment is encoded by one binary bit. A tree structure is employed
in the multi-step genetic algorithm and the examination is localized for root and
each branch. By this approach, we can avoid a long binary string that is required to
encode the segments when a large examination area is discretized using a required
resolution. Hence, for each step of examination, the standard genetic algorithm can
be locally applied and performed efficiently. We demonstrate the effectiveness of
the proposed method using an example of numerical simulation.
1 Introduction
Detection of reinforcement corrosion in the early stage is important to reduce
maintenance cost and increase the durability of the concrete structure [1].
The boundary element inverse analysis [2] has been introduced to estimate
the locations and sizes of plural corrosion in the concrete structure by modeling
them into a set of unknown parameters. The inverse analysis was carried out by
minimizing the cost-function using a down-hill simplex method. However, a such
method required to predetermine the number and shapes of plural corroded parts.
In addition, some difficulties were occured related with an appropriate initial guess
of the set of unknown parameters.